Accurate simulation of operating system updates in neuroimaging using\n Monte-Carlo arithmetic
Bibliographic record
Abstract
Operating system (OS) updates introduce numerical perturbations that impact\nthe reproducibility of computational pipelines. In neuroimaging, this has\nimportant practical implications on the validity of computational results,\nparticularly when obtained in systems such as high-performance computing\nclusters where the experimenter does not control software updates. We present a\nframework to reproduce the variability induced by OS updates in controlled\nconditions. We hypothesize that OS updates impact computational pipelines\nmainly through numerical perturbations originating in mathematical libraries,\nwhich we simulate using Monte-Carlo arithmetic in a framework called "fuzzy\nlibmath" (FL). We applied this methodology to pre-processing pipelines of the\nHuman Connectome Project, a flagship open-data project in neuroimaging. We\nfound that FL-perturbed pipelines accurately reproduce the variability induced\nby OS updates and that this similarity is only mildly dependent on simulation\nparameters. Importantly, we also found between-subject differences were\npreserved in both cases, though the between-run variability was of comparable\nmagnitude for both FL and OS perturbations. We found the numerical precision in\nthe HCP pre-processed images to be relatively low, with less than 8 significant\nbits among the 24 available, which motivates further investigation of the\nnumerical stability of components in the tested pipeline. Overall, our results\nestablish that FL accurately simulates results variability due to OS updates,\nand is a practical framework to quantify numerical uncertainty in neuroimaging.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".